Physiologic dead space is independently associated with mortality and discharge of mechanically ventilated patients with COVID-19 ARDS: a retrospective study

Physiologic dead space is a well-established independent predictor of death in patients with acute respiratory distress syndrome (ARDS). Here, we explore the association between a surrogate measure of dead space (DS) and early outcomes of mechanically ventilated patients admitted to Intensive Care Unit (ICU) because of COVID-19-associated ARDS. Retrospective cohort study on data derived from Italian ICUs during the first year of the COVID-19 epidemic. A competing risk Cox proportional hazard model was applied to test for the association of DS with two competing outcomes (death or discharge from the ICU) while adjusting for confounders. The final population consisted of 401 patients from seven ICUs. A significant association of DS with both death (HR 1.204; CI 1.019–1.423; p = 0.029) and discharge (HR 0.434; CI 0.414–0.456; p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.001$$\end{document}<0.001) was noticed even when correcting for confounding factors (age, sex, chronic obstructive pulmonary disease, diabetes, PaO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2/FiO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2, tidal volume, positive end-expiratory pressure, and systolic blood pressure). These results confirm the important association between DS and death or ICU discharge in mechanically ventilated patients with COVID-19-associated ARDS. Further work is needed to identify the optimal role of DS monitoring in this setting and to understand the physiological mechanisms underlying these associations.

This file contains the following supplementary information, supporting the results of the research: • A detailed mathematical explanation of the competing risk Cox proportional hazard model is provided in page 3.
• Table S1 shows demographic data and outcomes.
• Table S3 shows summary for first and last parameters recorded.
• Table S4 shows the obtained coefficients, standard errors, hazard ratios and p-values for the standardized model.
• Figure S1 shows the evolution of trends in the PaO 2 /FiO 2 ratio and Dead Space (DS).
The study was conducted following the criteria provided in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Checklist 1 .

Competing Risk Cox Proportional Hazard Model
Cox models are a class of survival models (also known as time to event analyses) whose main goal is to derive a survival function S(t) = P(T (r) ≥ t), i.e. the probability that an event r, among R possible events, has not occurred before time t. The defined probability is commonly modeled with an exponential function, where h(r) is the Hazard function for each of the R events: The Hazard function indeed represents the probability rate of having an event in a specific time window (t,t + ∆t) where ∆t is the duration of the window according to the following formulation: In a Competing Risk Cox Proportional Hazard framework the Hazard function is modeled as follows: where h i are the coefficients describing the link between the observations X i and the r event. Therefore, by exponentiating the estimated coefficient β (r) i , it is possible to extract the Hazard Ratio (HR (r) = exp(β (r) i )) of each variable and for each of the R competing outcomes. A HR (r) > 1 indicates an increased probability of experiencing the outcome r for an unitary increase of the corresponding variable while keeping the others constant. Similarly, a HR (r) < 1 indicates a reduced probability of experiencing the outcome r, whereas a HR (r) = 1 indicates no changes with respect to baseline hazard. Further details on the proposed modeling approach can be found in Austin et al. 2 . The proposed model contains the following features: PaO 2 /FiO 2 ratio, DS, positive end-expiratory pressure (PEEP), tidal volume (V T ), systolic blood pressure (SBP), age, sex, presence of diabetes, presence of chronic obstructive pulmonary disease (COPD). Covariates of the model were rescaled for better understanding of the hazard ratios. PaO 2 /FiO 2 was scaled by 50 to compute hazards for each 50 mmHg variation. DS, which ranges from 0-100%, was scaled by 10 to show each 10% variation of dead space fraction. V T was scaled by 10, so that any unitary change in the scaled V T reflects a 10 mL variation of the tidal volume. PEEP was scaled by a factor of 5, reflecting a 5 cmH 2 O variation for each unitary change. SBP was scaled by 20, reflecting a variation of 20 mmHg for any unitary change. Age was scaled by 10, thus reflecting a variation of 10 years for any unitary change.

Cohort Selection and Resulting Population
The initial population included data from distinct Italian ICUs during the first two waves of the COVID-19 epidemic in Italy, in the years 2020 and 2021. Population statistics are similar to those observed in comparable cohorts of critically-ill patients with COVID-19 3,4 , which also showed an ICU mortality rate between 30% and 40%, a middle-aged population, and a higher proportion of male patients. Hypertension and diabetes were also among the most prevalent comorbidities 3 . Our cohort selection process consisted of two main steps: the first was the exclusion of ICUs monitoring EtCO 2 in less than 50% of patients, while the second excluded patients that did not require invasive ventilation. The former case showed that the starting and the remaining populations had similar characteristics to each other, thus suggesting that the imposed recording requirements, although leading to a strong reduction in the numerosity of the population, did not introduce selection biases. In patients who did not require invasive ventilation, the data on pre-existing medical conditions and ICU outcomes agreed with those expected from a healthier population that did not require invasive ventilatory support, showing a lower mortality rate, a higher discharge rate, and a shorter length of stay.

Population Statistics Results
Demographic variables, pre-existing conditions and outcomes for the selected populations are shown in Tables S1 and S2. Tables S3a and S3b show summary statistics for the first and last set of physiologic variables recorded in the final population. Overall, a significantly lower median PaO 2 /FiO 2 ratio was observed in non-survivors (124; IQR 95-167) than in survivors (162; IQR 124-235; p<0.0001), already in the first set of recordings. This difference was even more marked in the last set of recordings, with a difference of more than 100 units in the median PaO 2 /FiO 2 ratio between non-survivors (111; IQR 79-157) and survivors (217; IQR 163-276; p<0.0001). Similarly, the first recorded median physiologic dead space was already significantly higher in non-survivors (33%; IQR, 22-41 %) than in survivors (22%; IQR, 12-34 %; p<0.0001). While the median DS further increased in non-survivors (37%; IQR, 28-44), it slightly decreased in surviving patients in the last recorded set of physiologic data (19%; IQR, 6-30%; p<0.0001). The dynamic course of the PaO 2 /FiO 2 and the DS in the final population was followed throughout the ICU stay, and is visually displayed in Figure S1. While no difference was observed between the median V T in survivors and non-survivors in the first recorded set of physiologic variables (450 mL; IQR, 410-506 mL in non-survivors; 451 mL; IQR, 398-500 mL in survivors; p=0.6201), a significantly lower median tidal volume was measured in non-survivors in the last set of recordings (442 mL; IQR, 372-508) as compared to survivors (480 mL; IQR, 404-570 mL; p=0.0003). Accordingly, while the median PEEP values did not significantly differ between survivors and non-survivors in the first measurements (12 cmH 2 O; IQR, 9-14 cmH 2    PEEP: positive end-expiratory pressure, in cmH 2 O. PaO 2 /FiO 2 : ratio of arterial oxygen partial pressure (PaO 2 , in mmHg) to fractional inspired oxygen (FiO 2 ). SBP: systolic blood pressure in mmHg. SEX: patient's sex (females = 1, males = 0). V T : tidal volume, in mL.